Sequential Fault Diagnosis Using an Inertial Velocity Differential Evolution Algorithm  

作　　者：Xiao-Hong Qiu Yu-Ting Hu Bo Li 

机构地区：[1]Software School,Jiangxi University of Science and Technology,Nanchang 330013,China [2]College of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China

出　　处：《International Journal of Automation and computing》2019年第3期389-397,共9页国际自动化与计算杂志（英文版）

基　　金：supported by National Natural Science Foundation of Jiangxi Province, China (No. 20132BAB201044);Jiangxi Higher Technology Landing Project, China (No. KJLD12071)

摘　　要：The optimal test sequence design for fault diagnosis is a challenging NP-complete problem.An improved differential evolution(DE)algorithm with additional inertial velocity term called inertial velocity differential evolution(IVDE)is proposed to solve the optimal test sequence problem(OTP)in complicated electronic system.The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm.And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate(FIR)satisfied and generate diagnostic decision tree to decrease the test sets and the test cost.The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR.Compared with the other algorithms such as particle swarm optimization(PSO)and genetic algorithm(GA),IVDE can get better solution to OTP.The optimal test sequence design for fault diagnosis is a challenging NP-complete problem. An improved differential evolution(DE) algorithm with additional inertial velocity term called inertial velocity differential evolution(IVDE) is proposed to solve the optimal test sequence problem(OTP) in complicated electronic system. The proposed IVDE algorithm is constructed based on adaptive differential evolution algorithm. And it is used to optimize the test sequence sets with a new individual fitness function including the index of fault isolation rate(FIR) satisfied and generate diagnostic decision tree to decrease the test sets and the test cost. The simulation results show that IVDE algorithm can cut down the test cost with the satisfied FIR. Compared with the other algorithms such as particle swarm optimization(PSO) and genetic algorithm(GA), IVDE can get better solution to OTP.

关 键 词：Differential evolution(DE) EVOLUTIONARY computation FAULT isolation rate(FIR) TESTABILITY FAULT diagnosis 

分 类 号：TP[自动化与计算机技术]